Info: Zenodo’s user support line is staffed on regular business days between Dec 23 and Jan 5. Response times may be slightly longer than normal.

Published February 28, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

Airport Runway Crack Detection to Classify and Densify Surface Crack Type

Creators

  • 1. Department of Software Engineering, Delhi Technological University, Delhi, India.

Contributors

Contact person:

  • 1. Department of Software Engineering, Delhi Technological University, Delhi, India.
  • 2. Department of Software Engineering, Delhi Technological University, Delhi, India

Description

Abstract: With the extensive development in infrastructures, many airports are built in order to satisfy travelling needs of people. The frequent arrival and departure of numerous plans lead to substantial runway damage and related safety concerns. So, the regular maintenance of runway has become an essential task specially for detection and classification of cracks in terms of owing to the intensity heterogeneity of cracks such as low real-time performance and the long time-consuming manual inspection. This paper introduces a new dataset named as ARID with 8 different crack classes. A runway crack detection model based on YOLOv5 and Faster RCNN has been proposed which is annotated on 8,228 collected datasets. Then the model is trained with different parameters for training to obtain the optimal result. Finally, based on experimental result, the crack detection precision has improved from 83% to 92%, while the recall has increased from 62.8% to 76%.

Files

A42731013123.pdf

Files (1.4 MB)

Name Size Download all
md5:a10284f5ef8d1d319881e66fbe2547e4
1.4 MB Preview Download

Additional details

Identifiers

DOI
10.35940/ijeat.A4273.13030224
EISSN
2249-8958

Dates

Accepted
2024-02-15
Manuscript received on 02 August 2023 | Revised Manuscript received on 19 January 2024 | Manuscript Accepted on 15 February 2024 | Manuscript published on 28 February 2024.

References

  • Gopalakrishnan, Kasthurirangan. "Deep learning in data-driven pavement image analysis and automated distress detection: A review." Data 3.3 (2018): 28. https://doi.org/10.3390/data3030028
  • Deleted.Oliveira, Henrique, and Paulo Lobato Correia. "CrackIT—An image processing toolbox for crack detection and characterization." 2014 IEEE international conference on image processing (ICIP). IEEE, 2014. https://doi.org/10.1109/ICIP.2014.7025160
  • Deleted.Oliveira, Henrique, and Paulo Lobato Correia. "CrackIT—An image processing toolbox for crack detection and characterization." 2014 IEEE international conference on image processing (ICIP). IEEE, 2014. https://doi.org/10.1109/ICIP.2014.7025160
  • Zhang, Allen, et al. "Automated pixel‐level pavement crack detection on 3D asphalt surfaces using a deep‐learning network." Computer‐Aided Civil and Infrastructure Engineering 32.10 (2017): 805-819. https://doi.org/10.1111/mice.12297
  • Zalama, Eduardo, et al. "Road crack detection using visual features extracted by Gabor filters." Computer‐Aided Civil and Infrastructure Engineering 29.5 (2014): 342-358. https://doi.org/10.1111/mice.12042
  • Akarsu, Büşra, et al. "A fast and adaptive road defect detection approach using computer vision with real time implementation." International Journal of Applied Mathematics Electronics and Computers Special Issue-1 (2016): 290-295. https://doi.org/10.18100/ijamec.270546
  • Chen, Jieh-Haur, et al. "A self organizing map optimization based image recognition and processing model for bridge crack inspection." Automation in Construction 73 (2017): 58-66. https://doi.org/10.1016/j.autcon.2016.08.033
  • Wang, Xianglong, and Zhaozheng Hu. "Grid-based pavement crack analysis using deep learning." 2017 4th international conference on transportation information and safety (ICTIS). IEEE, 2017. https://doi.org/10.1109/ICTIS.2017.8047878
  • Park, Somin, et al. "Patch-based crack detection in black box images using convolutional neural networks." Journal of Computing in Civil Engineering 33.3 (2019): 04019017. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000831
  • Li, Baoxian, et al. "Automatic classification of pavement crack using deep convolutional neural network." International Journal of Pavement Engineering 21.4 (2020): 457-463. https://doi.org/10.1080/10298436.2018.1485917
  • Dorafshan, Sattar, Robert J. Thomas, and Marc Maguire. "SDNET2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks." Data in brief 21 (2018): 1664-1668. https://doi.org/10.1016/j.dib.2018.11.015
  • Fan, Jiahe, et al. "Deep convolutional neural networks for road crack detection: Qualitative and quantitative comparisons." 2021 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, 2021. https://doi.org/10.1109/IST50367.2021.9651375
  • Wang, Kelvin CP, et al. "Deep learning for asphalt pavement cracking recognition using convolutional neural network." Airfield Highw. Pavements 2017 (2017): 166-177. https://doi.org/10.1061/9780784480922.015
  • Zhang, Ziyu, Sanja Fidler, and Raquel Urtasun. "Instance-level segmentation for autonomous driving with deep densely connected mrfs." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. https://doi.org/10.1109/CVPR.2016.79
  • Pham, Quang-Hieu, et al. "Real-time progressive 3D semantic segmentation for indoor scenes." 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2019. https://doi.org/10.1109/WACV.2019.00121
  • Zhou, Zongwei, et al. "Unet++: A nested u-net architecture for medical image segmentation." Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4. Springer International Publishing, 2018.
  • Kalfarisi, Rony, Zheng Yi Wu, and Ken Soh. "Crack detection and segmentation using deep learning with 3D reality mesh model for quantitative assessment and integrated visualization." Journal of Computing in Civil Engineering 34.3 (2020): 04020010. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000890
  • Kang, Dongho, et al. "Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning." Automation in Construction 118 (2020): 103291.
  • Ni, FuTao, Jian Zhang, and ZhiQiang Chen. "Zernike‐moment measurement of thin‐crack width in images enabled by dual‐scale deep learning." Computer‐Aided Civil and Infrastructure Engineering 34.5 (2019): 367-384. https://doi.org/10.1111/mice.12421
  • Zhang, Kaige, H. D. Cheng, and Boyu Zhang. "Unified approach to pavement crack and sealed crack detection using preclassification based on transfer learning." Journal of Computing in Civil Engineering 32.2 (2018): 04018001. https://doi.org/10.1061/(ASCE)CP.1943- 5487.0000736
  • Kim, Byunghyun, and Soojin Cho. "Image‐based concrete crack assessment using mask and region‐based convolutional neural network." Structural Control and Health Monitoring 26.8 (2019): e2381. https://doi.org/10.1002/stc.2381
  • Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. https://doi.org/10.1109/CVPR.2015.7298594
  • He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. https://doi.org/10.1109/CVPR.2016.90
  • Takikawa, Towaki, et al. "Gated-scnn: Gated shape cnns for semantic segmentation." Proceedings of the IEEE/CVF international conference on computer vision. 2019. https://doi.org/10.1109/ICCV.2019.00533
  • Zhang, Allen, et al. "Deep learning–based fully automated pavement crack detection on 3D asphalt surfaces with an improved CrackNet." Journal of Computing in Civil Engineering 32.5 (2018): 04018041. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000775
  • Fei, Yue, et al. "Pixel-level cracking detection on 3D asphalt pavement images through deep-learning-based CrackNet-V." IEEE Transactions on Intelligent Transportation Systems 21.1 (2019): 273-284. https://doi.org/10.1109/TITS.2019.2891167
  • Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer International Publishing, 2015.
  • Badrinarayanan, Vijay, Ankur Handa, and Roberto Cipolla. "Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling." arXiv preprint arXiv:1505.07293 (2015).
  • Jégou, Simon, et al. "The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2017 https://doi.org/10.1109/CVPRW.2017.156
  • Lee, Donghan, Jeongho Kim, and Daewoo Lee. "Robust concrete crack detection using deep learning-based semantic segmentation." International Journal of Aeronautical and Space Sciences 20 (2019): 287-299 https://doi.org/10.1007/s42405-018-0120-5
  • Yokoyama, Suguru, and Takashi Matsumoto. "Development of an automatic detector of cracks in concrete using machine learning." Procedia engineering 171 (2017): 1250-1255. https://doi.org/10.1016/j.proeng.2017.01.418
  • Cha, Young‐Jin, Wooram Choi, and Oral Büyüköztürk. "Deep learning‐based crack damage detection using convolutional neural networks." Computer‐Aided Civil and Infrastructure Engineering 32.5 (2017): 361-378. https://doi.org/10.1111/mice.12263
  • Pauly, Leo, et al. "Deeper networks for pavement crack detection." Proceedings of the 34th ISARC. IAARC, 2017. https://doi.org/10.22260/ISARC2017/0066
  • Feng, Chen, et al. "Deep active learning for civil infrastructure defect detection and classification." Computing in civil engineering 2017. 2017. 298-306. https://doi.org/10.1061/9780784480823.036
  • Eisenbach, Markus, et al. "How to get pavement distress detection ready for deep learning? A systematic approach." 2017 international joint conference on neural networks (IJCNN). IEEE, 2017. https://doi.org/10.1109/IJCNN.2017.7966101
  • Dorafshan, Sattar, et al. "Deep learning neural networks for sUASassisted structural inspections: Feasibility and application." 2018 international conference on unmanned aircraft systems (ICUAS). IEEE, 2018. https://doi.org/10.1109/ICUAS.2018.8453409
  • Silva, Wilson Ricardo Leal da, and Diogo Schwerz de Lucena. "Concrete cracks detection based on deep learning image classification." Proceedings. Vol. 2. No. 8. MDPI, 2018. https://doi.org/10.3390/ICEM18-05387
  • Kim, Hyunjun, et al. "Crack and noncrack classification from concrete surface images using machine learning." Structural Health Monitoring 18.3 (2019): 725-738. https://doi.org/10.1177/1475921718768747
  • Kim, Byunghyun, and Soojin Cho. "Automated vision-based detection of cracks on concrete surfaces using a deep learning technique." Sensors 18.10 (2018): 3452. https://doi.org/10.3390/s18103452
  • Zhang, Kaige, Heng-Da Cheng, and Shan Gai. "Efficient dense-dilation network for pavement cracks detection with large input image size." 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2018. https://doi.org/10.1109/ITSC.2018.8569958
  • Tan, Chenjun, Nasim Uddin, and Yahya M. Mohammed. "Deep learning-based crack detection using mask R-CNN technique." 9 th International Conference on Structural Health Monitoring of Intelligent Infrastructure. 2019. https://doi.org/10.1109/ICIP.2019.8803357
  • Fang, Fen, et al. "Towards real-time crack detection using a deep neural network with a Bayesian fusion algorithm." 2019 IEEE international conference on image processing (ICIP). IEEE, 2019. https://doi.org/10.1109/CVPR.2016.90
  • He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
  • Jenkins, Mark David, et al. "A deep convolutional neural network for semantic pixel-wise segmentation of road and pavement surface cracks." 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018.
  • Fan, Zhun, et al. "Automatic pavement crack detection based on structured prediction with the convolutional neural network." arXiv preprint arXiv:1802.02208 (2018).
  • Zhang, Allen, et al. "Automated pixel‐level pavement crack detection on 3D asphalt surfaces using a deep‐learning network." Computer‐Aided Civil and Infrastructure Engineering 32.10 (2017): 805-819. https://doi.org/10.1111/mice.12297
  • Li, Shengyuan, Xuefeng Zhao, and Guangyi Zhou. "Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network." Computer‐Aided Civil and Infrastructure Engineering 34.7 (2019): 616-634. https://doi.org/10.1111/mice.12433
  • Bang, Seongdeok, et al. "Encoder–decoder network for pixel‐level road crack detection in black‐box images." Computer‐Aided Civil and Infrastructure Engineering 34.8 (2019): 713-727. https://doi.org/10.1111/mice.12440
  • Zhang, Kaige, Yingtao Zhang, and Heng-Da Cheng. "CrackGAN: Pavement crack detection using partially accurate ground truths based on generative adversarial learning." IEEE Transactions on Intelligent Transportation Systems 22.2 (2020): 1306-1319. https://doi.org/10.1109/TITS.2020.2990703
  • Mei, Qipei, and Mustafa Gül. "Multi-level feature fusion in densely connected deep-learning architecture and depth-first search for crack segmentation on images collected with smartphones." Structural Health Monitoring 19.6 (2020): 1726-1744. https://doi.org/10.1177/1475921719896813
  • Chen, Tingyang, et al. "Pavement crack detection and recognition using the architecture of segNet." Journal of Industrial Information Integration 18 (2020): 100144. https://doi.org/10.1016/j.jii.2020.100144
  • Zou, Qin, et al. "Deepcrack: Learning hierarchical convolutional features for crack detection." IEEE Transactions on Image Processing 28.3 (2018): 1498-1512. https://doi.org/10.1109/TIP.2018.2878966
  • Yang, Fan, et al. "Feature pyramid and hierarchical boosting network for pavement crack detection." IEEE Transactions on Intelligent Transportation Systems 21.4 (2019): 1525-1535. https://doi.org/10.1109/TITS.2019.2910595
  • Mei, Qipei, and Mustafa Gül. "A cost effective solution for pavement crack inspection using cameras and deep neural networks." Construction and Building Materials 256 (2020): 119397. https://doi.org/10.1016/j.conbuildmat.2020.119397
  • Thuan, Do. "Evolution of Yolo algorithm and Yolov5: The State-ofthe-Art object detention algorithm." (2021)
  • Qiu, Qiwen, and Denvid Lau. "Real-time detection of cracks in tiled sidewalks using YOLO-based method applied to unmanned aerial vehicle (UAV) images." Automation in Construction 147 (2023): 104745. https://doi.org/10.1016/j.autcon.2023.104745
  • Deng, Jianghua, Ye Lu, and Vincent CS Lee. "A hybrid lightweight encoder-decoder network for automatic bridge crack assessment with real-world interference." Measurement 216 (2023): 112892. https://doi.org/10.1016/j.measurement.2023.112892
  • Chen, Zhuo, Xiaoke Huang, and Siqi Liu. "Pavement crack identification and detection based on multi-task learning." 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA). IEEE, 2023. https://doi.org/10.1109/EEBDA56825.2023.10090657
  • Kim, Bubryur, et al. "Surface crack detection using deep learning with shallow CNN architecture for enhanced computation." Neural Computing and Applications 33 (2021): 9289-9305. https://doi.org/10.1007/s00521-021-05690-8
  • Tang, Youzhi, et al. "Pixel-level pavement crack segmentation with encoder-decoder network." Measurement 184 (2021): 10 https://doi.org/10.1016/j.measurement.2021.109914